public MainWindow()
{
Formatter = d => d.ToString("0.####", CultureInfo.InvariantCulture);
_milliseconds = DateTimeOffset.Now.ToUnixTimeMilliseconds();
AlgorithmErrorValues = new ChartValues <ObservablePoint>();
AlgorithmValidationValues = new ChartValues <ObservablePoint>();
ActivationFunctionChartValues = new ChartValues <ObservablePoint>();
for (var i = -10.0; i < 10.0; i += 0.25)
{
ActivationFunctionChartValues.Add(new ObservablePoint(i, 0.0));
}
Function = new TanHFunction();
Layers = new ObservableCollection <LayerModel>();
InitializeComponent();
InitializeDataSetSettings();
InitializeNeuralNetworkSettings();
InitializeGeneticAlgorithmSettings();
DataContext = this;
}
public void Test1([Values(100, 200)] int populationSize,
[Values(10, 50, 250, 1000)] int iterations,
[Values(30)] int dataSize)
{
var neuralNetwork = new NeuralNetwork.NeuralNetwork {
MinWeight = -5, MaxWeight = 5
};
var activationFunction = new TanHFunction();
neuralNetwork.ActivationFunction = activationFunction;
neuralNetwork.AddLayer(new Layer(4, 2));
neuralNetwork.AddLayer(new Layer(1, 4));
var genetic = new GeneticAlgorithm.GeneticAlgorithm(populationSize, neuralNetwork.GetConnectionCount())
{
Selection = new TournamentSelection(populationSize, populationSize / 10),
Crossover = new TwoPointCrossover(0.65, populationSize),
Mutation = new FlipBitMutation(0.05)
};
var data = new List <SampleData>();
for (var i = 0; i < dataSize; i++)
{
data.Add(new SampleData());
}
for (var it = 0; it < iterations; it++)
{
foreach (var chromosome in genetic.Population)
{
neuralNetwork.SetWeights(chromosome.Genome);
foreach (var sampleData in data)
{
var res = neuralNetwork.GetResult(sampleData.Data);
lock (genetic)
{
chromosome.Fitness += Math.Abs(res[0] - sampleData.ObjectClass);
}
}
}
if (it < iterations - 1)
{
genetic.NextGeneration();
}
}
var best = genetic.Population.MinBy(chromosome => chromosome.Fitness).First();
neuralNetwork.SetWeights(best.Genome);
var correct = data.Count(d => Math.Abs(neuralNetwork.GetResult(d.Data)[0] - d.ObjectClass) < 0.3);
Console.WriteLine(
$@"Population: {populationSize}, iterations: {iterations} - fitness: {best.Fitness:0.0} " +
$@"| correctly recognized: {correct}");
foreach (var d in data)
{
var results = neuralNetwork.GetResult(d.Data)[0];
Console.WriteLine(
$@"[{d.Data[0]: 0.00;-0.00} {d.Data[1]: 0.00;-0.00}] - class: {d.ObjectClass: 0;-0} " +
$@"| recognized: {results: 0.00;-0.00} " +
$@"| is correct: {Math.Abs(results - d.ObjectClass) < 0.3}");
}
}
